Nina Brouwer

Application of a Driver Intention Recognition Algorithm on a Pedestrian Intention Recognition and Collision Avoidance System

Driver intention recognition can enhance the driver-vehicle interaction by offering more intuitive assistance and automated driving support. Especially urban environments require fast reactions and hence assistance systems which act in accordance to driver’s intentions. Assistance should provide comfortably timed warnings only in situations when drivers really need this support and not in situations when the driver is already intending to react to a thread.

Comparison and evaluation of pedestrian motion models for vehicle safety systems

In this paper the influence of different information on the performance of pedestrian motion models is evaluated. For this, besides of dynamic and physiological constraints, additional information as head orientation, typical pedestrian motion behavior and environment informations are used. A vehicle sensor system, consisting of a 12Mpx camera and Lidar provides the pedestrian and environment information in the considered test cases. Based on this, four pedestrian models, each based on different information, are used to predict the future position of the pedestrian. The predicted area is presented by a probability grid. The results show the performance of the models for evaluation parameters as prediction error, standard deviation and collision probability. Earlier works show, that the models have different performance depending on the prediction time. So a new introduced system fuses the probability map of each model to achieve a continuous prediction performance.